随机临床试验中的共变量调整,缺少共变量和结果数据
Chia-Rui Chang1, Yue Song1, Fan Li2
1Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.
Statistics in medicine
|July 3, 2023
概括
临床试验中缺少的数据可能会阻碍共变量调整. 新方法通过处理不完整的共同变量和结果数据来改善治疗效果估计,提高共同变量预测结果时的精度.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 数据分析 数据分析
背景情况:
- 随机临床试验 (RCT) 中的共变量调整可以提高治疗效果的精度.
- 缺失的数据对实施共变量调整构成重大挑战.
- 最近的理论进步为处理不完整数据提供了新的方法.
研究的目的:
- 审查和调查缺少共变量数据的RCT的共变量调整方法.
- 探索缺失数据机制对平均治疗效果估计的影响.
- 提出和评估一种新的全权重方法来处理缺失的结果和共变量.
主要方法:
- 对不完整数据的现有协变量调整技术的审查.
- 失踪数据机制的调查 (随机失踪,MAR).
- 建议采用全权重方法,将反向概率权重 (IPW) 和重叠权重结合起来.
- 模拟研究,以评估有限样本的性能.
主要成果:
- 建议的调整方法通常会提高治疗效果估计的精度.
- 当调整后的协变量与结果相关时,可以观察到更好的精度.
- 在不同的归算策略中,方法的有效性是稳定的.
- 适用于儿童腺切除术试验的应用证明了实际的实用性.
结论:
- 在RCT中,对缺少数据进行共变量调整是可行的和有益的.
- 拟议的全权重方法提供了一种可靠的方法来处理缺失的共变量和结果.
- 即使有不完整的数据,也可以准确估计治疗效果,特别是当共变量具有预测性时.
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